Fuller Christie M, Simmering Marcia J, Waterwall Brian, Ragland Elizabeth, Twitchell Douglas P, Wall Alison
Boise State University, ID, USA.
Louisiana Tech University, Ruston, LA, USA.
Educ Psychol Meas. 2025 Jan 29:00131644241311851. doi: 10.1177/00131644241311851.
Social and behavioral science researchers who use survey data are vigilant about data quality, with an increasing emphasis on avoiding common method variance (CMV) and insufficient effort responding (IER). Each of these errors can inflate and deflate substantive relationships, and there are both a priori and post hoc means to address them. Yet, little research has investigated how both IER and CMV are affected with the use of these different procedural or statistical techniques used to address them. More specifically, if interventions to reduce IER are used, does this affect CMV in data? In an experiment conducted both in and out of the laboratory, we investigate the impact of attentiveness interventions, such as a Factual Manipulation Check (FMC) on both IER and CMV in same-source survey data. In addition to typical IER measures, we also track whether respondents play the instructional video and their mouse movement. The results show that while interventions have some impact on the level of participant attentiveness, these interventions do not appear to lead to differing levels of CMV.
使用调查数据的社会和行为科学研究人员对数据质量保持警惕,越来越强调避免共同方法偏差(CMV)和回应努力不足(IER)。这些误差中的每一个都可能夸大或缩小实质性关系,并且有先验和事后方法来解决它们。然而,很少有研究调查使用这些不同的程序或统计技术来解决IER和CMV时,它们是如何受到影响的。更具体地说,如果使用减少IER的干预措施,这会影响数据中的CMV吗?在实验室内外进行的一项实验中,我们研究了诸如事实操纵检查(FMC)等注意力干预措施对同源调查数据中IER和CMV的影响。除了典型的IER测量方法外,我们还跟踪受访者是否观看教学视频及其鼠标移动情况。结果表明,虽然干预措施对参与者的注意力水平有一定影响,但这些干预措施似乎并没有导致不同水平的CMV。